Learning Gradient Fields for Molecular Conformation Generation
Authors: Chence Shi, Shitong Luo, Minkai Xu, Jian Tang
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results across multiple tasks show that Conf GF outperforms previous state-of-the-art baselines by a significant margin. The code is available at https://github.com/ Deep Graph Learning/Conf GF. |
| Researcher Affiliation | Academia | 1Mila Quebec AI Institute, Montr eal, Canada 2University of Montr eal, Montr eal, Canada 3Peking University 4CIFAR AI Research Chair 5HEC Montr eal, Montr eal, Canada. |
| Pseudocode | Yes | Algorithm 1 Annealed Langevin dynamics sampling |
| Open Source Code | Yes | The code is available at https://github.com/ Deep Graph Learning/Conf GF. |
| Open Datasets | Yes | Following Xu et al. (2021), we use the GEOM-QM9 and GEOM-Drugs (Axelrod & Gomez-Bombarelli, 2020) datasets for the conformation generation task... We evaluate the distance modeling task on the ISO17 dataset (Simm & Hernandez-Lobato, 2020). |
| Dataset Splits | No | The paper specifies a training set and a test set, but does not explicitly mention a validation set or a specific split for it. |
| Hardware Specification | Yes | The model is optimized with Adam (Kingma & Ba, 2014) optimizer on a single Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions "Conf GF is implemented in Pytorch (Paszke et al., 2017)", but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The GINs is implemented with N = 4 layers and the hidden dimension is set as 256 across all modules. For training, we use an exponentially-decayed learning rate starting from 0.001 with a decay rate of 0.95. The model is optimized with Adam (Kingma & Ba, 2014) optimizer on a single Tesla V100 GPU. All hyperparameters related to noise levels as well as annealed Langevin dynamics are selected according to Song & Ermon (2020). |